OpenRaiser/Intern-Atlas huggingface dataset:
Identity-sensitive Word Embedding through Heterogeneous Networks
Venue: arXiv cs.CL 2016
Year: 2016

Most existing word embedding approaches do not distinguish the same words in different contexts, therefore ignoring their contextual meanings. As a result, the learned embeddings of these words are usually a mixture of multiple meanings. In this paper, we acknowledge multiple identities of the same word in different contexts and learn the \textbf{identity-sensitive} word embeddings. Based on an identity-labeled text corpora, a heterogeneous network of words and word identities is constructed to model different-levels of word co-occurrences. The heterogeneous network is further embedded into a low-dimensional space through a principled network embedding approach, through which we are able to obtain the embeddings of words and the embeddings of word identities. We study three different types of word identities including topics, sentiments and categories. Experimental results on real-world data sets show that the identity-sensitive word embeddings learned by our approach indeed capture different meanings of words and outperforms competitive methods on tasks including text classification and word similarity computation.
Stub Wireless Multi-hop Networks using Self-configurable Wi-Fi Basic Service Set Cascading
Venue: arXiv cs.NI 2016
Year: 2016

The increasing trend in wireless Internet access has been boosted by IEEE 802.11. However, the application scenarios are still limited by its short radio range. Stub Wireless Multi-hop Networks (WMNs) are a robust, flexible, and cost-effective solution to the problem. Yet, typically they are formed by single radio mesh nodes and suffer from hidden node, unfairness, and scalability problems. We propose a simple multi-radio, multi-channel WMN solution, named Wi-Fi network Infrastructure eXtension - Dual-Radio (WiFIX-DR), to overcome these problems. WiFIX-DR reuses IEEE 802.11 built-in mechanisms and beacons to form a Stub WMN as a set of self-configurable interconnected Basic Service Sets (BSSs). Experimental results show the improved scalability enabled by the proposed solution when compared to single-radio WMNs.
Semantic Facial Expression Editing using Autoencoded Flow
Venue: arXiv cs.CV 2016
Year: 2016

High-level manipulation of facial expressions in images --- such as changing a smile to a neutral expression --- is challenging because facial expression changes are highly non-linear, and vary depending on the appearance of the face. We present a fully automatic approach to editing faces that combines the advantages of flow-based face manipulation with the more recent generative capabilities of Variational Autoencoders (VAEs). During training, our model learns to encode the flow from one expression to another over a low-dimensional latent space. At test time, expression editing can be done simply using latent vector arithmetic. We evaluate our methods on two applications: 1) single-image facial expression editing, and 2) facial expression interpolation between two images. We demonstrate that our method generates images of higher perceptual quality than previous VAE and flow-based methods.
Performance Limits of Energy Detection Systems with Massive Receiver Arrays
Venue: arXiv cs.IT 2016
Year: 2016

Energy detection (ED) is an attractive technique for symbol detection at receivers equipped with a large number of antennas, for example in millimeter wave communication systems. This paper investigates the performance bounds of ED with pulse amplitude modulation (PAM) in large antenna arrays under single stream transmission and fast fading assumptions. The analysis leverages information-theoretic tools and semi-numerical approach to provide bounds on the information rate, which are shown to be tight in the low and high signal-to-noise ratio (SNR) regimes, respectively. For a fixed constellation size, the impact of the number of antennas and SNR on the achievable information rate is investigated. Based on the results, heuristics are provided for the choice of the cardinality of the adaptive modulation scheme as a function of the SNR and the number of antennas.
Buddy-RAM: Improving the Performance and Efficiency of Bulk Bitwise Operations Using DRAM
Venue: arXiv cs.AR 2016
Year: 2016

Bitwise operations are an important component of modern day programming. Many widely-used data structures (e.g., bitmap indices in databases) rely on fast bitwise operations on large bit vectors to achieve high performance. Unfortunately, in existing systems, regardless of the underlying architecture (e.g., CPU, GPU, FPGA), the throughput of such bulk bitwise operations is limited by the available memory bandwidth. We propose Buddy, a new mechanism that exploits the analog operation of DRAM to perform bulk bitwise operations completely inside the DRAM chip. Buddy consists of two components. First, simultaneous activation of three DRAM rows that are connected to the same set of sense amplifiers enables us to perform bitwise AND and OR operations. Second, the inverters present in each sense amplifier enables us to perform bitwise NOT operations, with modest changes to the DRAM array. These two components make Buddy functionally complete. Our implementation of Buddy largely exploits the existing DRAM structure and interface, and incurs low overhead (1% of DRAM chip area). Our evaluations based on SPICE simulations show that, across seven commonly-used bitwise operations, Buddy provides between 10.9X---25.6X improvement in raw throughput and 25.1X---59.5X reduction in energy consumption. We evaluate three real-world data-intensive applications that exploit bitwise operations: 1) bitmap indices, 2) BitWeaving, and 3) bitvector-based implementation of sets. Our evaluations show that Buddy significantly outperforms the state-of-the-art.
Field study on requirements engineering: Investigation of artefacts, project parameters, and execution strategies
Venue: arXiv cs.SE 2016
Year: 2016

Requirements Engineering (RE) is a critical discipline mostly driven by uncertainty, since it is influenced by the customer domain or by the development process model used. We aim to investigate RE processes in successful project environments to discover characteristics and strategies that allow us to elaborate RE tailoring approaches in the future. We perform a field study on a set of projects at one company. First, we investigate by content analysis which RE artefacts were produced in each project and to what extent they were produced. Second, we perform qualitative analysis of semi-structured interviews to discover project parameters that relate to the produced artefacts. Third, we use cluster analysis to infer artefact patterns and probable RE execution strategies, which are the responses to specific project parameters. Fourth, we investigate by statistical tests the effort spent in each strategy in relation to the effort spent in change requests to evaluate the efficiency of execution strategies. Our results show no statistically significant difference between the efficiency of the strategies. In addition, it turned out that many parameters considered as the main causes for project failures can be successfully handled. Hence, practitioners can apply the artefact patterns and related project parameters to tailor the RE process according to individual project characteristics.
Active Deep Learning for Classification of Hyperspectral Images
Venue: arXiv cs.LG 2016
Year: 2016

Active deep learning classification of hyperspectral images is considered in this paper. Deep learning has achieved success in many applications, but good-quality labeled samples are needed to construct a deep learning network. It is expensive getting good labeled samples in hyperspectral images for remote sensing applications. An active learning algorithm based on a weighted incremental dictionary learning is proposed for such applications. The proposed algorithm selects training samples that maximize two selection criteria, namely representative and uncertainty. This algorithm trains a deep network efficiently by actively selecting training samples at each iteration. The proposed algorithm is applied for the classification of hyperspectral images, and compared with other classification algorithms employing active learning. It is shown that the proposed algorithm is efficient and effective in classifying hyperspectral images.
Using Temporal and Semantic Developer-Level Information to Predict Maintenance Activity Profiles
Venue: arXiv cs.SE 2016
Year: 2016

Predictive models for software projects' characteristics have been traditionally based on project-level metrics, employing only little developer-level information, or none at all. In this work we suggest novel metrics that capture temporal and semantic developer-level information collected on a per developer basis. To address the scalability challenges involved in computing these metrics for each and every developer for a large number of source code repositories, we have built a designated repository mining platform. This platform was used to create a metrics dataset based on processing nearly 1000 highly popular open source GitHub repositories, consisting of 147 million LOC, and maintained by 30,000 developers. The computed metrics were then employed to predict the corrective, perfective, and adaptive maintenance activity profiles identified in previous works. Our results show both strong correlation and promising predictive power with R-squared values of 0.83, 0.64, and 0.75. We also show how these results may help project managers to detect anomalies in the development process and to build better development teams. In addition, the platform we built has the potential to yield further predictive models leveraging developer-level metrics at scale.
An Efficient Algorithm for Vertex Enumeration of Two-Dimensional Projection of Polytopes
Venue: arXiv cs.CG 2016
Year: 2016

An efficient algorithm to enumerate the vertices of a two-dimensional (2D) projection of a polytope, is presented in this paper. The proposed algorithm uses the support function of the polytope to be projected and enumerated for vertices. The complexity of our algorithm is linear in the number of vertices of the projected polytope and we show empirically that the performance is significantly better in comparison to some known efficient algorithms of projection and enumeration.
System-Generated Requests for Rewriting Proposals
Venue: arXiv cs.AI 2016
Year: 2016

We present an online deliberation system using mutual evaluation in order to collaboratively develop solutions. Participants submit their proposals and evaluate each other's proposals; some of them may then be invited by the system to rewrite 'problematic' proposals. Two cases are discussed: a proposal supported by many, but not by a given person, who is then invited to rewrite it for making yet more acceptable; and a poorly presented but presumably interesting proposal. The first of these cases has been successfully implemented. Proposals are evaluated along two axes-understandability (or clarity, or, more generally, quality), and agreement. The latter is used by the system to cluster proposals according to their ideas, while the former is used both to present the best proposals on top of their clusters, and to find poorly written proposals candidates for rewriting. These functionalities may be considered as important components of a large scale online deliberation system.
Deep encoding of etymological information in TEI
Venue: arXiv cs.CL 2016
Year: 2016

This paper aims to provide a comprehensive modeling and representation of etymological data in digital dictionaries. The purpose is to integrate in one coherent framework both digital representations of legacy dictionaries, and also born-digital lexical databases that are constructed manually or semi-automatically. We want to propose a systematic and coherent set of modeling principles for a variety of etymological phenomena that may contribute to the creation of a continuum between existing and future lexical constructs, where anyone interested in tracing the history of words and their meanings will be able to seamlessly query lexical resources.Instead of designing an ad hoc model and representation language for digital etymological data, we will focus on identifying all the possibilities offered by the TEI guidelines for the representation of lexical information.
Matching Multiplications in Bit-Vector Formulas
Venue: arXiv cs.LO 2016
Year: 2016

Bit-vector formulas arising from hardware verification problems often contain word-level arithmetic operations. Empirical evidence shows that state-of-the-art SMT solvers are not very efficient at reasoning about bit-vector formulas with multiplication. This is particularly true when multiplication operators are decomposed and represented in alternative ways in the formula.We present a pre-processing heuristic that identifies certain types of decomposed multipliers, and adds special assertions to the input formula encoding the equivalence of sub-terms to word-level multiplication. The pre-processed formulas are then solved using an SMT solver. Our experiments with three SMT solvers show that our heuristic allows several formulas to be solved quickly, while the same formulas time out without the pre-processing step.
A Bayesian Network Approach to Assess and Predict Software Quality Using Activity-Based Quality Models (Conference Version)
Venue: arXiv cs.SE 2016
Year: 2016

Assessing and predicting the complex concept of software quality is still challenging in practice as well as research. Activity-based quality models break down this complex con- cept into more concrete definitions, more precisely facts about the system, process and environment and their impact on ac- tivities performed on and with the system. However, these models lack an operationalisation that allows to use them in assessment and prediction of quality. Bayesian Networks (BN) have been shown to be a viable means for assessment and prediction incorporating variables with uncertainty. This paper describes how activity-based quality models can be used to derive BN models for quality assessment and pre- diction. The proposed approach is demonstrated in a proof of concept using publicly available data.
Behavior-Based Machine-Learning: A Hybrid Approach for Predicting Human Decision Making
Venue: arXiv cs.LG 2016
Year: 2016

A large body of work in behavioral fields attempts to develop models that describe the way people, as opposed to rational agents, make decisions. A recent Choice Prediction Competition (2015) challenged researchers to suggest a model that captures 14 classic choice biases and can predict human decisions under risk and ambiguity. The competition focused on simple decision problems, in which human subjects were asked to repeatedly choose between two gamble options. In this paper we present our approach for predicting human decision behavior: we suggest to use machine learning algorithms with features that are based on well-established behavioral theories. The basic idea is that these psychological features are essential for the representation of the data and are important for the success of the learning process. We implement a vanilla model in which we train SVM models using behavioral features that rely on the psychological properties underlying the competition baseline model. We show that this basic model captures the 14 choice biases and outperforms all the other learning-based models in the competition. The preliminary results suggest that such hybrid models can significantly improve the prediction of human decision making, and are a promising direction for future research.
Assessing pattern recognition or labeling in streams of temporal data
Venue: arXiv cs.IR 2016
Year: 2016

In the data deluge context, pattern recognition or labeling in streams is becoming quite an essential and pressing task as data flows inside always bigger streams. The assessment of such tasks is not so easy when dealing with temporal data, namely patterns that have a duration (a beginning and an end time-stamp). This paper details an approach based on an editing distance to first align a sequence of labeled temporal segments with a ground truth sequence, and then, by back-tracing an optimal alignment path, to provide a confusion matrix at the label level. From this confusion matrix, standard evaluation measures can easily be derived as well as other measures such as the "latency" that can be quite important in (early) pattern detection applications.
The observer-assisted method for adjusting hyper-parameters in deep learning algorithms
Venue: arXiv cs.LG 2016
Year: 2016

This paper presents a concept of a novel method for adjusting hyper-parameters in Deep Learning (DL) algorithms. An external agent-observer monitors a performance of a selected Deep Learning algorithm. The observer learns to model the DL algorithm using a series of random experiments. Consequently, it may be used for predicting a response of the DL algorithm in terms of a selected quality measurement to a set of hyper-parameters. This allows to construct an ensemble composed of a series of evaluators which constitute an observer-assisted architecture. The architecture may be used to gradually iterate towards to the best achievable quality score in tiny steps governed by a unit of progress. The algorithm is stopped when the maximum number of steps is reached or no further progress is made.
SLA Violation Prediction In Cloud Computing: A Machine Learning Perspective
Venue: arXiv cs.DC 2016
Year: 2016

Service level agreement (SLA) is an essential part of cloud systems to ensure maximum availability of services for customers. With a violation of SLA, the provider has to pay penalties. In this paper, we explore two machine learning models: Naive Bayes and Random Forest Classifiers to predict SLA violations. Since SLA violations are a rare event in the real world (~0.2 %), the classification task becomes more challenging. In order to overcome these challenges, we use several re-sampling methods. We find that random forests with SMOTE-ENN re-sampling have the best performance among other methods with the accuracy of 99.88 % and F_1 score of 0.9980.
The Right Invariant Nonlinear Complementary Filter for Low Cost Attitude and Heading Estimation of Platforms
Venue: arXiv cs.RO 2016
Year: 2016

This paper presents a novel filter with low computational demand to address the problem of orientation estimation of a robotic platform. This is conventionally addressed by extended Kalman filtering of measurements from a sensor suit which mainly includes accelerometers, gyroscopes, and a digital compass. Low cost robotic platforms demand simpler and computationally more efficient methods to address this filtering problem. Hence nonlinear observers with constant gains have emerged to assume this role. The nonlinear complementary filter is a popular choice in this domain which does not require covariance matrix propagation and associated computational overhead in its filtering algorithm. However, the gain tuning procedure of the complementary filter is not optimal, where it is often hand picked by trial and error. This process is counter intuitive to system noise based tuning capability offered by a stochastic filter like the Kalman filter. This paper proposes the right invariant formulation of the complementary filter, which preserves Kalman like system noise based gain tuning capability for the filter. The resulting filter exhibits efficient operation in elementary embedded hardware, intuitive system noise based gain tuning capability and accurate attitude estimation. The performance of the filter is validated using numerical simulations and by experimentally implementing the filter on an ARDrone 2.0 micro aerial vehicle platform.
BASS Net: Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification
Venue: arXiv cs.CV 2016
Year: 2016

Deep learning based landcover classification algorithms have recently been proposed in literature. In hyperspectral images (HSI) they face the challenges of large dimensionality, spatial variability of spectral signatures and scarcity of labeled data. In this article we propose an end-to-end deep learning architecture that extracts band specific spectral-spatial features and performs landcover classification. The architecture has fewer independent connection weights and thus requires lesser number of training data. The method is found to outperform the highest reported accuracies on popular hyperspectral image data sets.
Playing Doom with SLAM-Augmented Deep Reinforcement Learning
Venue: arXiv cs.AI 2016
Year: 2016

A number of recent approaches to policy learning in 2D game domains have been successful going directly from raw input images to actions. However when employed in complex 3D environments, they typically suffer from challenges related to partial observability, combinatorial exploration spaces, path planning, and a scarcity of rewarding scenarios. Inspired from prior work in human cognition that indicates how humans employ a variety of semantic concepts and abstractions (object categories, localisation, etc.) to reason about the world, we build an agent-model that incorporates such abstractions into its policy-learning framework. We augment the raw image input to a Deep Q-Learning Network (DQN), by adding details of objects and structural elements encountered, along with the agent's localisation. The different components are automatically extracted and composed into a topological representation using on-the-fly object detection and 3D-scene reconstruction.We evaluate the efficacy of our approach in Doom, a 3D first-person combat game that exhibits a number of challenges discussed, and show that our augmented framework consistently learns better, more effective policies.
Distributed Nash Equilibrium Seeking via the Alternating Direction Method of Multipliers
Venue: arXiv cs.SY 2016
Year: 2016

In this paper, the problem of finding a Nash equilibrium of a multi-player game is considered. The players are only aware of their own cost functions as well as the action space of all players. We develop a relatively fast algorithm within the framework of inexact-ADMM. It requires a communication graph for the information exchange between the players as well as a few mild assumptions on cost functions. The convergence proof of the algorithm to a Nash equilibrium of the game is then provided. Moreover, the convergence rate is investigated via simulations.
A Visual Representation for Editing Face Images
Venue: arXiv cs.CV 2016
Year: 2016

We propose a new approach for editing face images, which enables numerous exciting applications including face relighting, makeup transfer and face detail editing. Our face edits are based on a visual representation, which includes geometry, face segmentation, albedo, illumination and detail map. To recover our visual representation, we start by estimating geometry using a morphable face model, then decompose the face image to recover the albedo, and then shade the geometry with the albedo and illumination. The residual between our shaded geometry and the input image produces our detail map, which carries high frequency information that is either insufficiently or incorrectly captured by our shading process. By manipulating the detail map, we can edit face images with reality and identity preserved. Our representation allows various applications. First, it allows a user to directly manipulate various illumination. Second, it allows non-parametric makeup transfer with input face's distinctive identity features preserved. Third, it allows non-parametric modifications to the face appearance by transferring details. For face relighting and detail editing, we evaluate via a user study and our method outperforms other methods. For makeup transfer, we evaluate via an online attractiveness evaluation system, and can reliably make people look younger and more attractive. We also show extensive qualitative comparisons to existing methods, and have significant improvements over previous techniques.